Abstract
Index tracking consists of mimicking a benchmark performance with a portfolio formed by a subset of assets contained in the index. Due to the cardinality constraint, obtaining an optimal solution for this problem can be impractical as the number of stocks in the index grows. Then, meta-heuristics, such as genetic algorithms, can obtain good solutions in a reason- able time, making it possible for the investor to run different configurations of the problem before deciding to rebalance or not his portfolio. Also, to evaluate an investment strategy, it is important to perform backtests considering different risk scenarios, especially in crisis scenarios, with a high volatile market. This work aims to analyze the integration of hybrid and pure genetic algorithms and two optimization models in a high volatility market scenario, the Brazilian market index IBOVESPA during the COVID-19 pandemic. We observed that the hybrid algorithms returned competitive solutions, tracking IBOVESPA even closer than the CPLEX solution on the linear model for a non-rebalancing strategy. However, they were not competitive in a rebalancing strategy, with solutions presenting a gap of more than 100% relative to the general-purpose solver solution.
Published Version
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